{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad28c5fe-0187-4493-b274-725b5beea851",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cantera as ct\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "from scipy.interpolate import interp1d\n",
    "\n",
    "p0 = 101325\n",
    "T0 = 300\n",
    "phi = 0.7 # equivalence ratio\n",
    "alpha = 0.6\n",
    "width = 0.02 # 2cm\n",
    "loglevel = 0\n",
    "\n",
    "xh2 = alpha / (1 - alpha) * 1\n",
    "fuel = {'CH4': 1, 'H2': xh2}\n",
    "oxidizer = {'O2': 1, 'N2': 3.76}\n",
    "\n",
    "gas = ct.Solution('gri30.yaml')\n",
    "gas.TP = T0, p0\n",
    "gas.set_equivalence_ratio(phi, fuel, oxidizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0489f2b4-f45e-421d-ad24-9eeed87959aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flame speed: 0.34056 m/s\n"
     ]
    }
   ],
   "source": [
    "flame = ct.FreeFlame(gas, width=width)\n",
    "flame.solve(loglevel, auto=True)\n",
    "flame_speed = flame.velocity[0] #Sl0\n",
    "print(f'Flame speed: {flame_speed:.5f} m/s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22ced447-a355-40b8-bcdf-e21bd244b82b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adiabatic temperature (Tad) from FreeFlame: 1820.61 K\n"
     ]
    }
   ],
   "source": [
    "def get_adiabatic_temperature_from_flame(flame): # lamninar unstretched flame\n",
    "    Tad = flame.T[-1]  # final status of the flame\n",
    "    return Tad\n",
    "\n",
    "Tad = get_adiabatic_temperature_from_flame(flame) # laminar unstretched configuration\n",
    "print(f'Adiabatic temperature (Tad) from FreeFlame: {Tad:.2f} K')\n",
    "Xflame = flame.X[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "122b4e5c-4e8c-4214-a2c2-82cb5e57fba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "cp = flame.cp_mass[0]\n",
    "density = flame.density_mass[0]    \n",
    "lambda_ = flame.thermal_conductivity[0]\n",
    "thermal_diffusivity = lambda_ / (density * cp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b89939b-9283-40f2-b5c0-6eb28439687c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_heat_loss_coefficient(Tp, Tad, Tr):\n",
    "    beta = (Tp - Tr) / (Tad - Tr)\n",
    "    return beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9fb8edf6-f8cf-4d8c-92c9-c1f6897ef36e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2\n",
      "0.4\n",
      "0.6\n",
      "0.8\n"
     ]
    }
   ],
   "source": [
    "Tp = [605, 909, 1213, 1517] #beta 0.2, 0.4, 0.6, 0.8\n",
    "for temp in Tp:\n",
    "    beta = calculate_heat_loss_coefficient(temp, Tad, T0)\n",
    "    print(f'{beta:.1f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5aa55c12-c317-42ea-8462-b9500695ebac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_consumption_speed(flame_cf, gas, fuel_species):\n",
    "    rho_u = flame_cf.density[0]\n",
    "    total_fuel_mass_fraction = sum(flame_cf.Y[gas.species_index(fuel), 0] for fuel in fuel_species)\n",
    "\n",
    "    integral_numerator = 0\n",
    "    for fuel in fuel_species:\n",
    "        i_fuel = gas.species_index(fuel)\n",
    "        nk = flame_cf.Y[i_fuel, 0] / total_fuel_mass_fraction\n",
    "        integral = scipy.integrate.simpson(gas.molecular_weights[i_fuel] * flame_cf.net_production_rates[i_fuel], x=flame_cf.grid)\n",
    "        integral_numerator += nk * integral\n",
    "\n",
    "    mass_fraction_diff = sum(flame_cf.Y[gas.species_index(fuel), -1] - flame_cf.Y[gas.species_index(fuel), 0] for fuel in fuel_species)\n",
    "    S_cF = integral_numerator / (rho_u * mass_fraction_diff)\n",
    "    return S_cF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f099b07f-601f-4d8f-9dfb-d431f449bfe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0016734670101391021, 0.0017353637410183183, 0.0022870990023536692, 0.002850714682045305, 0.0035090168339713687, 0.004284900945530291, 0.005194751357196241, 0.006259579500505982, 0.007513249778334362, 0.0092564746703561, 0.011029752670251554, 0.013105097961635242, 0.015518251809668865, 0.018319964921496446, 0.02155359309357879, 0.025289470163722103, 0.029585826075231594, 0.034527390406243315, 0.04016723315399465, 0.04660359038902699, 0.053938919821059556, 0.06222499040743416, 0.07155551077283062, 0.084660872256915, 0.09857426264029237, 0.1158047807048331, 0.13597065494516772, 0.1593688708972552, 0.18651744582673582, 0.21792936248365585, 0.2542202147904929, 0.29614470640727, 0.014296492775362008, 0.015758121905853517, 0.017356440730801136, 0.019132463731565526, 0.021112238452178748, 0.023283647855560252, 0.02566533718149298, 0.02827869944387197, 0.031154110808521148, 0.03434088713532089, 0.03782026747281166, 0.04163242456800028, 0.04580184578061076, 0.05046676653444171, 0.05557773882064674, 0.06117953249657119, 0.0672803960625692, 0.07394617768821403, 0.0814421191402594, 0.08963639650501047, 0.09858342012263822, 0.10835813051673743, 0.11905403895293701, 0.13099340565115825, 0.1442032748633417, 0.1586240537071598, 0.17438801942884002, 0.19160149118670405, 0.21078073152858215, 0.2318054860916067, 0.2547204549702209, 0.27964637912425044, 0.30726775395675965, 0.3378898698769516, 0.37131409995960113, 0.407789716657854, 0.4475038139734161, 0.49214032919723316, 0.5412072034024256, 0.5945721902160457, 0.6529433210356252, 0.7172527874905649, 0.7884076815496505, 0.8658680648055153, 0.9503479920344724, 1.0447973654904452, 1.1479665207365835, 1.2605190534673931, 1.383205935587735, 1.5185826647753282, 1.66957431039565, 1.8343280107956388, 2.0136216526173416, 2.208862554478413, 2.433728315447216, 2.671353280343202, 2.934202240691173, 3.2215339345244627, 3.535463699227046, 3.8774388072257926, 4.259069319702417, 4.68033857084368, 5.143693702951752, 5.6448890585207145, 6.193467167709446, 6.80071918493048, 7.465773764261797, 8.192583971911704]\n",
      "[0.011379912126218975, 0.011827033569763354, 0.013626054843870954, 0.015326100733796387, 0.017103164368845535, 0.01897829714285082, 0.0209621454657099, 0.023078385098154793, 0.02531653303279322, 0.02792667610748067, 0.0305137642908752, 0.03329647446356002, 0.03628625688592291, 0.03949273170345854, 0.0429051785417643, 0.04656953366115705, 0.05050619586943973, 0.05472411361694034, 0.0592014762712893, 0.06400948518028739, 0.06914548135460288, 0.07464907265199873, 0.08050210548201774, 0.08754217473848928, 0.09433453850451874, 0.1016428028639179, 0.1094892650215748, 0.11782732566548938, 0.12672069274616962, 0.13622491232502904, 0.1463295530613875, 0.15714633465291508, 1.5954930427200222e-06, 1.6700004134254556e-06, 1.7537757075521097e-06, 1.84611505644599e-06, 1.9467254015361486e-06, 2.06046546581039e-06, 2.178399869513722e-06, 2.30035219557395e-06, 2.431607025910724e-06, 2.569401428912842e-06, 2.7133256162134228e-06, 2.868778797419885e-06, 3.024250018775168e-06, 3.199812596754101e-06, 3.392763686088357e-06, 3.5914697430894e-06, 3.7961095204801806e-06, 4.0234627445696255e-06, 4.267785824949174e-06, 4.534500643460645e-06, 4.7667814287407886e-06, 5.0711265413371895e-06, 5.400298040993703e-06, 5.761703199803108e-06, 6.1470245616114835e-06, 6.56198658744772e-06, 6.978901881196349e-06, 7.468162253992809e-06, 8.000384818666119e-06, 8.563225839708462e-06, 9.19379422890246e-06, 9.884969232597968e-06, 1.0615779412406098e-05, 1.1377201329289635e-05, 1.2257416142345656e-05, 1.3210394493299657e-05, 1.4250722018344514e-05, 1.5367214166079287e-05, 1.6634983481380228e-05, 1.796881911081036e-05, 1.9466921462764212e-05, 2.1072885093626443e-05, 2.281034387040646e-05, 2.4695227496611376e-05, 2.6722596510611447e-05, 2.8757647140253514e-05, 3.1225968631536085e-05, 3.3970257819161285e-05, 3.685887672909897e-05, 3.976274929230852e-05, 4.29804751233396e-05, 4.684552101534732e-05, 5.074209461442163e-05, 5.54657502063466e-05, 5.96898036668465e-05, 6.542002025235328e-05, 7.158741191016084e-05, 7.857566916074e-05, 8.570982876489662e-05, 9.355932654496623e-05, 0.0001029202416937668, 0.00011267943105692911, 0.00012293968471800286, 0.00013509271026428924, 0.00014936469326523035, 0.00016515300144908716, 0.00018066412752657733, 0.00019850232436471229]\n"
     ]
    }
   ],
   "source": [
    "mdot_values = np.logspace(-2, 2, 100)  # From 0.01 to 100 with 100 points\n",
    "Ka1_values = []\n",
    "ScF1_values = []\n",
    "\n",
    "# Loop through mdot values\n",
    "for mdot in mdot_values:\n",
    "    # Initialize gas object in each iteration to avoid carrying over previous states\n",
    "    gas = ct.Solution('gri30.yaml')\n",
    "    gas.TP = T0, p0\n",
    "    gas.set_equivalence_ratio(phi, fuel, oxidizer)\n",
    "    \n",
    "    flame_cf = ct.CounterflowPremixedFlame(gas=gas, width=width)\n",
    "    flame_cf.transport_model = 'multicomponent'\n",
    "    flame_cf.energy_enabled = True\n",
    "    flame.soret_enabled = True\n",
    "    flame_cf.set_refine_criteria(ratio=3, slope=0.1, curve=0.2, prune=0.02)\n",
    "    flame_cf.reactants.mdot = mdot\n",
    "    flame_cf.products.mdot = mdot\n",
    "    flame_cf.products.T = Tp  # beta=0.2\n",
    "    flame_cf.products.X = Xflame\n",
    "    flame_cf.set_initial_guess(equilibrate=False)\n",
    "    \n",
    "    flame_cf.solve(loglevel, auto=True)\n",
    "\n",
    "    # Update temperature for current mdot\n",
    "    Tp = 605 # beta=0.2\n",
    "    \n",
    "    # Calculate the maximum gradient |du/dx|\n",
    "    grad_u = np.gradient(flame_cf.velocity, flame_cf.grid) \n",
    "    max_grad_idx = np.argmax(np.abs(grad_u))  # find max |du/dx| index\n",
    "    max_grad_value = np.abs(grad_u[max_grad_idx])\n",
    "    \n",
    "    # Calculate strain rate (Ka)\n",
    "    Ka = (thermal_diffusivity/flame_speed) / flame_speed * max_grad_value\n",
    "    Ka1_values.append(Ka)\n",
    "    \n",
    "    ScF = calculate_consumption_speed(flame_cf, gas, ['CH4', 'H2'])\n",
    "    ScF1_values.append(ScF)\n",
    "\n",
    "print(Ka1_values)\n",
    "print(ScF1_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3772b25a-3be7-48b4-bfd6-edc90382c80f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.03341501993452775, 0.03472790985700506, 0.04001040510566725, 0.045002277333800585, 0.05022029801194629, 0.055726280688114034, 0.061551486587787425, 0.0677654352299268, 0.07433734519046652, 0.08200155049410002, 0.08959805934774369, 0.09776897621080483, 0.10654792266795553, 0.11596314650792322, 0.12598316932205275, 0.1367428744940803, 0.1483021593731081, 0.16068729943848745, 0.1738342517778189, 0.18795208605963226, 0.20303299465056984, 0.21919327874364905, 0.23637963365775438, 0.25705150281950806, 0.27699603034547315, 0.2984554051233749, 0.32149509879636434, 0.34597828105132944, 0.3720920185731973, 0.3999994121600083, 0.429669832097499, 0.4614313227399067, 4.684872012768651e-06, 4.903649209795964e-06, 5.149639960182642e-06, 5.420777483021801e-06, 5.716201265694223e-06, 6.050178055051121e-06, 6.3964707510759034e-06, 6.754561337467671e-06, 7.139967017543304e-06, 7.544574950549545e-06, 7.96718186828042e-06, 8.42364155718538e-06, 8.880154217670944e-06, 9.3956614533909e-06, 9.962226856091879e-06, 1.0545690663382923e-05, 1.1146577749774005e-05, 1.1814158696873294e-05, 1.2531568507317973e-05, 1.3314727540405857e-05, 1.39967773650843e-05, 1.4890430838994187e-05, 1.585698243455166e-05, 1.6918182244542624e-05, 1.8049607588009157e-05, 1.9268067292407323e-05, 2.0492262408951566e-05, 2.192888555057508e-05, 2.3491659270698084e-05, 2.5144338459210293e-05, 2.6995886613651208e-05, 2.9025394949969645e-05, 3.117128469461728e-05, 3.340706017768361e-05, 3.599164916208959e-05, 3.878989489906099e-05, 4.1844625427930346e-05, 4.512299936961884e-05, 4.8845570903854286e-05, 5.276213402424104e-05, 5.7161036177375085e-05, 6.187665314736463e-05, 6.697838143995508e-05, 7.251299570106087e-05, 7.84659921096513e-05, 8.4441544170426e-05, 9.168931646613655e-05, 9.974741716968503e-05, 0.0001082293155699754, 0.00011675600351894595, 0.00012620426389171497, 0.00013755326062357402, 0.00014899483267134374, 0.00016286497894464893, 0.000175268135403293, 0.00019209386299329058, 0.000210203274815098, 0.00023072300754639955, 0.00025167115571704817, 0.0002747197629371994, 0.00030220636940948695, 0.0003308624348955786, 0.0003609897835794215, 0.0003966749089468096, 0.00043858196334162593, 0.00048494142788270487, 0.0005304869981241612, 0.0005828655838576131]\n"
     ]
    }
   ],
   "source": [
    "y1_values = []\n",
    "for i in ScF1_values:\n",
    "    temp = i / flame_speed\n",
    "    y1_values.append(temp)\n",
    "print(y1_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cdba2e77-5298-4929-a1fd-f22f25eca26d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.001319458029503298, 0.002120705692634718, 0.0025771304008546272, 0.0031192527716023028, 0.0037593164180056435, 0.0045164466088012285, 0.00540830278353772, 0.006456178688402562, 0.007690848401827364, 0.009425134908953355, 0.0111824617162207, 0.013238914197615147, 0.01563277957044329, 0.018415722960317873, 0.021630774509300194, 0.025352164625518276, 0.029636491992134088, 0.03456937069554475, 0.04020986751958261, 0.046635495589118336, 0.05397181566785334, 0.06226099208025205, 0.07159679209721205, 0.08473024421640021, 0.09841019595594602, 0.11562678367069311, 0.13577225540002508, 0.15914675469708947, 0.18628224784919387, 0.2176813680781372, 0.2539411920874552, 0.2958492373499432, 0.3440099299236088, 0.40277590044962985, 0.024537605834686855, 0.027068661814066922, 0.029803803848715605, 0.03281788521117946, 0.036204919145560184, 0.039907385946180034, 0.04396166192921858, 0.04837389541900431, 0.05317814509497609, 0.05847138744614414, 0.06461894981963978, 0.07118702948371813, 0.07834943285982406, 0.08615330665322202, 0.0951996732645585, 0.10478236418249265, 0.11531484983621147, 0.1266845507446818, 0.13945969158121646, 0.1534574260693366, 0.16873618609369886, 0.1854468106820697, 0.2035501285867033, 0.2239003736686727, 0.24633850841003962, 0.271014451494675, 0.29812435622600714, 0.327901108915639, 0.36010209735682625, 0.3955294409042191, 0.43609257199434576, 0.4792922424663154, 0.526270684507043, 0.5773047547393536, 0.6349698709135799, 0.6981385307300467, 0.7665395355961694, 0.8410038465623885, 0.9244374610980447, 1.017642965809502, 1.1192071605108767, 1.2289048099250066, 1.350650217282515, 1.482808898149751, 1.6268999002800604, 1.7864107113246757, 1.9658319799902244, 2.1602798281070363, 2.3710937274393595, 2.6019864495534835, 2.858657103703803, 3.137314798420278, 3.4463641094377864, 3.783398530532546, 4.152491320167499, 4.560195571184876, 5.003413559490276, 5.491553865741283, 6.037580300910148, 6.627509846079006, 7.273873831541058, 7.986493877225999, 8.764765638804349, 9.620913071734513, 10.560751912484239, 11.588872961544169]\n",
      "[0.010126122253810658, 0.01285807587843388, 0.014316597558826296, 0.015878768805560464, 0.017540975921437295, 0.019323020187326824, 0.021235460036904144, 0.023293901808633534, 0.02547709494100394, 0.028040719457410716, 0.030592286891529024, 0.0333567186062417, 0.03631396307112699, 0.039497064603069745, 0.042891208612200284, 0.04654322844397278, 0.0504707997374983, 0.05468327653983151, 0.0591607164821106, 0.0639638676113053, 0.06910046918105828, 0.07460460349143998, 0.08045953965422727, 0.08750405689464084, 0.09429548320399096, 0.10160454037309995, 0.10945148109383739, 0.11778978694184986, 0.12668321192735263, 0.13618742085293462, 0.14629115422215638, 0.15710666568074358, 0.16851588219238262, 0.18155979167752925, 5.5421600593671e-06, 5.794755035740494e-06, 6.1381479827713035e-06, 6.5139149145004035e-06, 6.935390413509388e-06, 7.397212910577884e-06, 7.923807306791736e-06, 8.465060286557287e-06, 9.059030669298777e-06, 9.687651324562788e-06, 1.0345491466885626e-05, 1.1076921718965258e-05, 1.1855530057506074e-05, 1.262913811988499e-05, 1.3333533659040553e-05, 1.4275540059885944e-05, 1.5361588074854168e-05, 1.6459935735711822e-05, 1.771959963793978e-05, 1.8990510649806744e-05, 2.0369234574656297e-05, 2.1870453459770586e-05, 2.3483294304928784e-05, 2.5281524838297572e-05, 2.7064656064212356e-05, 2.8991803062261296e-05, 3.120807889778892e-05, 3.378648544801602e-05, 3.644233370737778e-05, 3.931928069917553e-05, 4.229308630574961e-05, 4.579539641487709e-05, 4.942494497980278e-05, 5.310351246744461e-05, 5.724446957205929e-05, 6.226703643365151e-05, 6.745651391846712e-05, 7.324832470548986e-05, 7.889972633976143e-05, 8.561520041441981e-05, 9.244862283362889e-05, 9.954156553642796e-05, 0.0001083557552882096, 0.0001175647455398266, 0.00012811973188669982, 0.00014073831633455933, 0.0001519522901840142, 0.00016531047944752219, 0.00017814042195232293, 0.00019312781776639206, 0.00021106481668940549, 0.00023042075313526255, 0.00025234384908170597, 0.00027582517204795184, 0.00030307700454706835, 0.00033289125292990915, 0.00036088110758484887, 0.00039459823921696993, 0.0004259277438800725, 0.00046703774464947815, 0.0005153039482606555, 0.0005670656213378032, 0.0006205638818059789, 0.0006884976903704737, 0.0007532023296709588, 0.0008288773784952687]\n"
     ]
    }
   ],
   "source": [
    "mdot_values = np.logspace(-2, 2, 100)  # From 0.01 to 100 with 100 points\n",
    "Ka2_values = []\n",
    "ScF2_values = []\n",
    "\n",
    "# Loop through mdot values\n",
    "for mdot in mdot_values:\n",
    "    # Initialize gas object in each iteration to avoid carrying over previous states\n",
    "    gas = ct.Solution('gri30.yaml')\n",
    "    gas.TP = T0, p0\n",
    "    gas.set_equivalence_ratio(phi, fuel, oxidizer)\n",
    "    \n",
    "    flame_cf2 = ct.CounterflowPremixedFlame(gas=gas, width=width)\n",
    "    flame_cf2.transport_model = 'multicomponent'\n",
    "    flame_cf2.energy_enabled = True\n",
    "    flame.soret_enabled = True\n",
    "    flame_cf2.set_refine_criteria(ratio=3, slope=0.1, curve=0.2, prune=0.02)\n",
    "    flame_cf2.reactants.mdot = mdot\n",
    "    flame_cf2.products.mdot = mdot\n",
    "    flame_cf2.products.T = Tp  # beta=0.4\n",
    "    flame_cf2.products.X = Xflame\n",
    "    flame_cf2.set_initial_guess(equilibrate=False)\n",
    "    \n",
    "    flame_cf2.solve(loglevel, auto=True)\n",
    "\n",
    "    # Update temperature for current mdot\n",
    "    Tp = 909 # beta=0.4\n",
    "    \n",
    "    # Calculate the maximum gradient |du/dx|\n",
    "    grad_u = np.gradient(flame_cf2.velocity, flame_cf2.grid) \n",
    "    max_grad_idx = np.argmax(np.abs(grad_u))  # find max |du/dx| index\n",
    "    max_grad_value = np.abs(grad_u[max_grad_idx])\n",
    "    \n",
    "    # Calculate strain rate (Ka)\n",
    "    Ka = (thermal_diffusivity/flame_speed) / flame_speed * max_grad_value\n",
    "    Ka2_values.append(Ka)\n",
    "    \n",
    "    ScF = calculate_consumption_speed(flame_cf2, gas, ['CH4', 'H2'])\n",
    "    ScF2_values.append(ScF)\n",
    "\n",
    "print(Ka2_values)\n",
    "print(ScF2_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ea06c848-de2c-4e6d-bb53-98c8b43b1172",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.029733496464438104, 0.0377553760549373, 0.04203805684233688, 0.046625085526899486, 0.051505851151123376, 0.056738496536118475, 0.06235402458136392, 0.06839826043077163, 0.07480880571705756, 0.08233641782603073, 0.08982862652937537, 0.09794587206081837, 0.10662927678744571, 0.11597586927109371, 0.12594214919204752, 0.1366656340596336, 0.14819822513870368, 0.16056738886883415, 0.1737145681499642, 0.18781813845441403, 0.202900824677761, 0.21906270322864846, 0.23625464692952902, 0.25693957677843415, 0.27688135163519756, 0.29834305435306613, 0.321384153238565, 0.34586805549028654, 0.3719819630390208, 0.3998893253444411, 0.42955708096514056, 0.46131484209566354, 0.49481590897081085, 0.5331169512491722, 1.6273534172326318e-05, 1.7015232884694397e-05, 1.8023543146069255e-05, 1.912691366204494e-05, 2.0364498983011702e-05, 2.172055584659703e-05, 2.3266803484692185e-05, 2.485609335862881e-05, 2.6600178195109912e-05, 2.844600718692674e-05, 3.0377633830932622e-05, 3.252534430381464e-05, 3.481158455461482e-05, 3.708314241369046e-05, 3.915147042199257e-05, 4.191749904449652e-05, 4.510647938700775e-05, 4.833157537857009e-05, 5.2030346857373886e-05, 5.576214340602223e-05, 5.981051275387634e-05, 6.421856603370869e-05, 6.895437667920689e-05, 7.423455006305523e-05, 7.947038710634338e-05, 8.512909998938974e-05, 9.163678655174401e-05, 9.9207803385578e-05, 0.00010700621356180315, 0.0001154538395201806, 0.00012418587299486253, 0.00013446976278376168, 0.00014512726490725346, 0.00015592870208591626, 0.00016808786137150717, 0.0001828357056378903, 0.0001980736522653843, 0.00021508023990497445, 0.00023167454187960232, 0.00025139329696185895, 0.0002714583856748758, 0.00029228550799178973, 0.0003181667558428969, 0.00034520726278369123, 0.00037620003981725354, 0.0004132521933131736, 0.00044617996600321703, 0.0004854038330751696, 0.0005230766006503269, 0.0005670843332531069, 0.0006197530331455407, 0.0006765881822236787, 0.0007409613232419801, 0.0008099099114475453, 0.0008899299077454382, 0.000977473967224621, 0.0010596610299089175, 0.0011586651885637663, 0.00125065851955307, 0.001371370479362777, 0.0015130953132580743, 0.0016650839506511886, 0.0018221717576728234, 0.002021646897922892, 0.0022116401762629512, 0.0024338460454264455]\n"
     ]
    }
   ],
   "source": [
    "y2_values = []\n",
    "for i in ScF2_values:\n",
    "    temp = i / flame_speed\n",
    "    y2_values.append(temp)\n",
    "print(y2_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8e39805d-eb7a-492a-8b54-3df184650f76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0016734670101391021, 0.002532072543501373, 0.002986094812389616, 0.0035224127125849047, 0.004155220641805998, 0.004903652619542243, 0.0057847328242507405, 0.007021278050200706, 0.00828661930877391, 0.00976790102996653, 0.011507402024380758, 0.013537591957367536, 0.015903276001646097, 0.018659257150416185, 0.021852796094084304, 0.025546160700332533, 0.029804933107800878, 0.03471567296057943, 0.040328823397715007, 0.04673726002032646, 0.0540590690111093, 0.06234320685123513, 0.07166727515126137, 0.0848038377802556, 0.09826304195635664, 0.11546274253892283, 0.13558625876473582, 0.15894700911038695, 0.18605911832321284, 0.21746554416229746, 0.25367464635007947, 0.29558524003287595, 0.343694144907997, 0.40244473733804687, 0.46696452611363326, 0.5398953760328671, 0.619695461725769, 0.7140420018722087, 0.8219576560094713, 0.9370082406202977, 1.0130918175105337, 1.0209400502996444, 1.0215022583604598, 1.0002726334208254, 1.007156278150477, 1.0158403022958113, 1.0143945483675259, 1.025231164129012, 1.0249663151751671, 1.0330527358704347, 1.0292358919599065, 1.0364737555928, 1.0353519272842626, 1.0341396355552104, 1.0339527728587854, 1.0296807764796299, 1.026359966208397, 1.0216958573017334, 1.0129724593653677, 0.99724368756512, 0.9807371338232609, 0.9560315691578335, 0.9264752284593454, 0.8811682543708351, 0.818009719939395, 0.731559949884988, 0.6422187992609407, 0.7405365415499997, 0.8130757869295002, 0.893374824600823, 0.9812102449298248, 1.078027595913867, 1.1835435514309283, 1.3000722309326067, 1.4283474025049492, 1.5684464561578935, 1.7227699466550026, 1.8917892001960084, 2.077928535902239, 2.28217101703284, 2.5058072450388833, 2.752093366236131, 3.02140501073556, 3.318690234672381, 3.643607317801238, 4.000801665145014, 4.39557715699272, 4.825807124402835, 5.299951182039911, 5.820984033181583, 6.392124243118955, 7.017570105691912, 7.701840548770271, 8.458270117635466, 9.282426213546477, 10.189355234194906, 11.188646548906371, 12.275903588788523, 13.48190324804585, 14.791078387476293]\n",
      "[0.011379912126218975, 0.014028674889790316, 0.015342726487662443, 0.016772010913235023, 0.018322255655242228, 0.020000500247516866, 0.021802712805399856, 0.023923686391352568, 0.02605509360528608, 0.02837306278166391, 0.03087511065169974, 0.03357585732147033, 0.03648788783989883, 0.03961316490195071, 0.04298479786200653, 0.04661225064655377, 0.05051375169132149, 0.054689675727739966, 0.05916411600261597, 0.06395519634942173, 0.06908299453671157, 0.07458794490801313, 0.08043252134344203, 0.08747610735543783, 0.09426650776205583, 0.10157575864609676, 0.10942173101973543, 0.11775966495186754, 0.12665248791130862, 0.13615770963202692, 0.14625963283175783, 0.15707069243922062, 0.16848080423556952, 0.1815247501713793, 0.19457105471549677, 0.20803344347779468, 0.22138775957213977, 0.2363954972753495, 0.2516558480035087, 0.2648835030009266, 0.26782459394342684, 0.2694328800034476, 0.2700961891146117, 0.267565540265475, 0.26924262339727734, 0.27100049362148004, 0.2715756477348435, 0.2738512078120096, 0.2744714733953898, 0.27641569712884134, 0.2767970031318537, 0.27876622256875494, 0.2796171817778885, 0.28055492569734053, 0.28149242430876803, 0.28206669893387293, 0.28314632709702825, 0.28396211176261277, 0.28433902334566286, 0.2840537517694773, 0.28404367314503653, 0.2827396932351535, 0.28074702787897426, 0.2766638939674852, 0.27017966605728516, 0.2599762939591633, 0.2430805702425834, 0.022597815886388622, 0.02043382678816876, 0.01921332488960937, 0.017372446953341916, 0.016550388179729574, 0.015370461559481114, 0.014290460982064582, 0.01334224698562695, 0.012523362612820187, 0.011657788142455517, 0.010945063433250867, 0.010286231101803264, 0.009705519825994347, 0.00904211293761757, 0.008584465731229285, 0.008159040466255768, 0.007700910588212002, 0.007280255351690515, 0.007000790367460933, 0.006684094100006902, 0.006410094034037835, 0.006185519836272352, 0.006126869236044373, 0.005943031630755166, 0.005800243308249841, 0.005706312742043778, 0.005658707691572686, 0.0055947608387272455, 0.005552074688785505, 0.005577174413372045, 0.005576769144628747, 0.005620335108377873, 0.005681295660650637]\n"
     ]
    }
   ],
   "source": [
    "mdot_values = np.logspace(-2, 2, 100)  # From 0.01 to 100 with 100 points\n",
    "Ka3_values = []\n",
    "ScF3_values = []\n",
    "\n",
    "# Loop through mdot values\n",
    "for mdot in mdot_values:\n",
    "    # Initialize gas object in each iteration to avoid carrying over previous states\n",
    "    gas = ct.Solution('gri30.yaml')\n",
    "    gas.TP = T0, p0\n",
    "    gas.set_equivalence_ratio(phi, fuel, oxidizer)\n",
    "    \n",
    "    flame_cf = ct.CounterflowPremixedFlame(gas=gas, width=width)\n",
    "    flame_cf.transport_model = 'multicomponent'\n",
    "    flame_cf.energy_enabled = True\n",
    "    flame.soret_enabled = True\n",
    "    flame_cf.set_refine_criteria(ratio=3, slope=0.1, curve=0.2, prune=0.02)\n",
    "    flame_cf.reactants.mdot = mdot\n",
    "    flame_cf.products.mdot = mdot\n",
    "    flame_cf.products.T = Tp # beta=0.6\n",
    "    flame_cf.products.X = Xflame\n",
    "    flame_cf.set_initial_guess(equilibrate=False)\n",
    "    \n",
    "    flame_cf.solve(loglevel, auto=True)\n",
    "\n",
    "    # Update temperature for current mdot\n",
    "    Tp = 1213 # beta=0.6\n",
    "    \n",
    "    # Calculate the maximum gradient |du/dx|\n",
    "    grad_u = np.gradient(flame_cf.velocity, flame_cf.grid) \n",
    "    max_grad_idx = np.argmax(np.abs(grad_u))  # find max |du/dx| index\n",
    "    max_grad_value = np.abs(grad_u[max_grad_idx])\n",
    "    \n",
    "    # Calculate strain rate (Ka)\n",
    "    Ka = (thermal_diffusivity/flame_speed) / flame_speed * max_grad_value\n",
    "    Ka3_values.append(Ka)\n",
    "    \n",
    "    ScF = calculate_consumption_speed(flame_cf, gas, ['CH4', 'H2'])\n",
    "    ScF3_values.append(ScF)\n",
    "    \n",
    "print(Ka3_values)\n",
    "print(ScF3_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "04998f2d-0078-458d-b5ff-1c164ebf3cfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.03341501993452775, 0.04119262485492518, 0.04505109580363577, 0.049247926767082416, 0.05379993546302116, 0.058727792188440604, 0.06401965805524014, 0.07024750708158974, 0.0765059925384408, 0.08331228290142455, 0.09065908650826121, 0.09858933261276064, 0.1071399748975154, 0.11631677645547993, 0.12621695681914052, 0.13686830506882838, 0.14832434565504068, 0.16058617890773377, 0.17372455021697797, 0.1877926768879728, 0.20284951359708503, 0.21901378836084323, 0.23617531262681454, 0.25685750809466307, 0.27679627057665024, 0.29825854210280495, 0.3212967976150642, 0.34577960780417416, 0.3718917476140516, 0.39980208380620263, 0.42946452419696435, 0.4612092132850658, 0.49471290899917914, 0.5330140583082119, 0.5713221332369459, 0.6108519629815988, 0.6500644572043279, 0.6941319200250217, 0.7389411345544092, 0.7777817117507317, 0.7864176846284553, 0.7911401209847255, 0.7930878062503468, 0.7856570211262364, 0.7905814674364957, 0.7957431301921081, 0.7974319645126461, 0.8041137283520639, 0.805935024211247, 0.8116438797885045, 0.8127635147690292, 0.8185457656343861, 0.8210444509160726, 0.8237979635455573, 0.8265507558731243, 0.8282370077380716, 0.8314071373659266, 0.8338025390667871, 0.8349092706409402, 0.8340716231003206, 0.8340420290724524, 0.8302131317839949, 0.8243620362868546, 0.8123726641779606, 0.7933329209465386, 0.7633725945154919, 0.7137614078441085, 0.0663543320849307, 0.06000017591455765, 0.05641639646018664, 0.05101099681780406, 0.04859717235211932, 0.04513253474335932, 0.04196131158948903, 0.03917705550370069, 0.03677255208241612, 0.03423095177291851, 0.03213816668795875, 0.0302036266629869, 0.02849847475652774, 0.026550502386096235, 0.025206705496029526, 0.023957522413412435, 0.022612308246761265, 0.021377131475796233, 0.020556533925001553, 0.019626613555424586, 0.018822062732459635, 0.01816264188527885, 0.017990425179732015, 0.017450619847553608, 0.0170313481879825, 0.016755538348027267, 0.01661575486878486, 0.0164279866910625, 0.016302646658959336, 0.01637634738600432, 0.016375157388842133, 0.016503080832450275, 0.016682080287527]\n"
     ]
    }
   ],
   "source": [
    "y3_values = []\n",
    "for i in ScF3_values:\n",
    "    temp = i / flame_speed\n",
    "    y3_values.append(temp)\n",
    "print(y3_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "214d9658-9cd6-4ce2-8c2f-f6f8dbe670ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.002146796117206929, 0.0029983448356444404, 0.0034679023077742173, 0.004017416322407892, 0.004661386998213138, 0.005568425832658812, 0.006481711306077262, 0.007549894971549075, 0.008806506568908608, 0.01027580495985137, 0.01199150853475691, 0.013996094002551446, 0.016333357348800724, 0.01905445626442839, 0.022207504472413257, 0.025861486035125714, 0.03008384880726804, 0.03495429409917445, 0.040533599085470204, 0.04691172336224479, 0.0542057814422311, 0.06246597613385026, 0.07177575574585497, 0.08490273803606271, 0.09813380459988286, 0.11530931871667247, 0.13540961453574776, 0.15875736793391715, 0.18584763051107836, 0.21721532509419744, 0.25342318137397357, 0.29530489761668854, 0.34339662519116587, 0.40213028331172274, 0.46661286332317253, 0.5395059630455, 0.6192381127312191, 0.7134547301323995, 0.821218098625007, 0.9364969921888262, 1.0176785789524547, 1.0212643136108097, 0.9933567955203974, 0.9988826796282406, 1.0141172831434622, 1.0135840275092327, 1.0197936543143984, 1.0159226185400456, 1.026862417373596, 1.0277740589667894, 1.0309318192210466, 1.0323144382071052, 1.0325571577307766, 1.036045582425401, 1.0325249729774855, 1.0296109771812245, 1.027825706356843, 1.0224021932818732, 1.0143491052322655, 1.0045174745756398, 0.9891650672732714, 0.9751360839235853, 0.9493475634541919, 0.9243112382794558, 0.8915053621777913, 0.8501294388980075, 0.8013431227755836, 0.7993316021423729, 0.8717872743841041, 0.9516376146317559, 1.0419099987204032, 1.1416870587755341, 1.251740054126169, 1.3736813431825745, 1.511508797142219, 1.6662629389320112, 1.839980001041379, 2.0359303064501697, 2.256600916620242, 2.5025908284896996, 2.774649261605531, 3.0746842910003, 3.4061963061365788, 3.7695326423315323, 4.16857408220127, 4.606838106010617, 5.088027321396749, 5.615976322182544, 6.196641595014874, 6.8322681352637, 7.528142855121984, 8.297731089135338, 9.137760818451943, 10.062575248543904, 11.075268952450886, 12.183664247742659, 13.402344793535898, 14.73048021027454, 16.19680145549999, 17.795005949874504]\n",
      "[0.012827277795769946, 0.015356532660230843, 0.01657217220053277, 0.017872688657031607, 0.01934237980486514, 0.021033951334847582, 0.022769875806603126, 0.02465983054432075, 0.026705369651998795, 0.028931829225884248, 0.031354602085369596, 0.03396477544079028, 0.03680282116827165, 0.039880906657024755, 0.043195692272396745, 0.04677154968650356, 0.050637979899721845, 0.05477412807306211, 0.05923144272201453, 0.06398709507189303, 0.06909622116148972, 0.07458768117221125, 0.08042592891001911, 0.087463729412959, 0.0942516042259078, 0.1015567259167489, 0.10940085236354238, 0.11773901559938123, 0.1266319078839486, 0.13617269043507693, 0.1462385783217083, 0.15705151698699213, 0.16845984444015877, 0.18151057299898174, 0.1945554817167399, 0.20801550672276312, 0.2213642499870807, 0.23635920741251348, 0.25162158001393053, 0.2646435374392759, 0.26875443959197287, 0.26971408042982997, 0.26622264219148334, 0.26770318454462244, 0.27048436673580684, 0.2710809955349363, 0.2726505506556824, 0.2728812888573606, 0.27519605244395035, 0.27617902416793705, 0.2777065852023188, 0.2789117172333651, 0.27998925724715074, 0.28146045399220204, 0.28213580034852354, 0.2831863321545858, 0.28434541474974634, 0.28510621390848384, 0.28571602790625167, 0.28614230824376274, 0.2859418570465487, 0.2859702383211476, 0.2847087036620374, 0.28340785573171534, 0.28121511247777015, 0.2780439648818323, 0.27378483492141925, 0.2676140129856897, 0.26014206402937956, 0.2505699406211394, 0.23972288322453736, 0.22817096070115686, 0.2151567728237038, 0.20197754705808108, 0.19053507933547842, 0.17953554604961955, 0.17089006970326084, 0.16296579764842162, 0.15543711481488753, 0.148971462948481, 0.1427397579450302, 0.13691602241873865, 0.130913260636552, 0.12534356772902133, 0.11999766609956256, 0.11443289215162229, 0.1093712202583835, 0.10377483431875499, 0.09823988526997697, 0.09319464098587407, 0.08837326627180479, 0.08248911726616548, 0.07657126917317543, 0.07117409148070132, 0.06568533681889459, 0.06103670076877365, 0.056537072841496464, 0.05185535423480288, 0.048131498911197564, 0.04500654828292467]\n"
     ]
    }
   ],
   "source": [
    "mdot_values = np.logspace(-2, 2, 100)  # From 0.01 to 100 with 100 points\n",
    "Ka4_values = []\n",
    "ScF4_values = []\n",
    "\n",
    "# Loop through mdot values\n",
    "for mdot in mdot_values:\n",
    "    # Initialize gas object in each iteration to avoid carrying over previous states\n",
    "    gas = ct.Solution('gri30.yaml')\n",
    "    gas.TP = T0, p0\n",
    "    gas.set_equivalence_ratio(phi, fuel, oxidizer)\n",
    "    \n",
    "    flame_cf = ct.CounterflowPremixedFlame(gas=gas, width=width)\n",
    "    flame_cf.transport_model = 'multicomponent'\n",
    "    flame_cf.energy_enabled = True\n",
    "    flame.soret_enabled = True\n",
    "    flame_cf.set_refine_criteria(ratio=3, slope=0.1, curve=0.2, prune=0.02)\n",
    "    flame_cf.reactants.mdot = mdot\n",
    "    flame_cf.products.mdot = mdot\n",
    "    flame_cf.products.T = Tp  # beta=0.8\n",
    "    flame_cf.products.X = Xflame\n",
    "    flame_cf.set_initial_guess(equilibrate=False)\n",
    "    \n",
    "    flame_cf.solve(loglevel, auto=True)\n",
    "\n",
    "    # Update temperature for current mdot\n",
    "    Tp = 1517 # beta=0.8\n",
    "    \n",
    "    # Calculate the maximum gradient |du/dx|\n",
    "    grad_u = np.gradient(flame_cf.velocity, flame_cf.grid) \n",
    "    max_grad_idx = np.argmax(np.abs(grad_u))  # find max |du/dx| index\n",
    "    max_grad_value = np.abs(grad_u[max_grad_idx])\n",
    "    \n",
    "    # Calculate strain rate (Ka)\n",
    "    Ka = (thermal_diffusivity/flame_speed) / flame_speed * max_grad_value\n",
    "    Ka4_values.append(Ka)\n",
    "    \n",
    "    ScF = calculate_consumption_speed(flame_cf, gas, ['CH4', 'H2'])\n",
    "    ScF4_values.append(ScF)\n",
    "\n",
    "print(Ka4_values)\n",
    "print(ScF4_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f6b0d992-b1fa-4148-a1e0-06d78ad5f98c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.037664943146954684, 0.0450916350913276, 0.04866113712454633, 0.05247986461884454, 0.056795342494047905, 0.0617623313220911, 0.06685955441003587, 0.07240905905785185, 0.07841540860623172, 0.08495299784414803, 0.09206702495601063, 0.09973131917345063, 0.10806471872056998, 0.11710295089899712, 0.1268362094855151, 0.13733605741493038, 0.14868911895153783, 0.16083415769449041, 0.1739222427514964, 0.1878863416224138, 0.20288835114923187, 0.21901301394914485, 0.23615595515798693, 0.2568211625420708, 0.2767525090825404, 0.29820265599195334, 0.32123549127937734, 0.3457189747766681, 0.3718313181470345, 0.3998460721803658, 0.42940270149875603, 0.46115290809471265, 0.4946513644130957, 0.5329724296477244, 0.5712764059889558, 0.6107992949978488, 0.6499954256296824, 0.6940253615141442, 0.7388405129026626, 0.7770770969929706, 0.7891480054374018, 0.7919658143423671, 0.7817138478033875, 0.7860611882481413, 0.7942276184744226, 0.7959795092619175, 0.8005882193351029, 0.801265739646615, 0.808062617384485, 0.8109489331475406, 0.8154343353060924, 0.8189729839700282, 0.8221369821311336, 0.8264568809157857, 0.8284399113389469, 0.8315246048631216, 0.8349280378240687, 0.8371619846923238, 0.8389525913917216, 0.8402042852376532, 0.8396156971469441, 0.8396990335423373, 0.835994768929859, 0.8321750681236154, 0.8257364806609699, 0.8164249887128507, 0.8039188365604938, 0.785799352358173, 0.7638593478523708, 0.7357525671536715, 0.7039021771754098, 0.669982080331118, 0.6317683100898822, 0.5930699364297471, 0.5594712334863355, 0.5271731260896491, 0.5017872741381675, 0.47851910600627073, 0.45641251289966267, 0.4374273148059165, 0.4191290586676601, 0.40202872990008587, 0.3844027234433355, 0.36804834412387305, 0.35235108675183907, 0.33601115105293783, 0.321148482049991, 0.3047157235488821, 0.2884633631835158, 0.27364893083482994, 0.2594918503235254, 0.2422141284799476, 0.2248374554614301, 0.2089896353566775, 0.19287291631651549, 0.17922305113651377, 0.1660107209163611, 0.1522637148907097, 0.14132929830722135, 0.13215345526203892]\n"
     ]
    }
   ],
   "source": [
    "y4_values = []\n",
    "for i in ScF4_values:\n",
    "    temp = i / flame_speed\n",
    "    y4_values.append(temp)\n",
    "print(y4_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "660ceafd-13d1-47d9-a449-97c1d8d88f50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.plot(Ka4_values, y1_values, label='beta=0.2')\n",
    "plt.plot(Ka4_values, y2_values, label='beta=0.4')\n",
    "plt.plot(Ka4_values, y3_values, label='beta=0.6')\n",
    "plt.plot(Ka4_values, y4_values, label='beta=0.8')\n",
    "\n",
    "# 그래프 설정\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Ka')\n",
    "plt.ylabel('ScF/Sl0')\n",
    "plt.title('Strain rate vs. Consumption Speed')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d15f18ff-292f-4cfd-a8ba-71f3a8e5da32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.plot(mdot_values, y1_values, label='beta=0.2')\n",
    "plt.plot(mdot_values, y2_values, label='beta=0.4')\n",
    "plt.plot(mdot_values, y3_values, label='beta=0.6')\n",
    "plt.plot(mdot_values, y4_values, label='beta=0.8')\n",
    "\n",
    "# 그래프 설정\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Mass Flow Rate (mdot_reactants)')\n",
    "plt.ylabel('ScF/Sl0')\n",
    "plt.title('Mass Flow Rate vs. Consumption Speed')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "39fff665-7d59-42ae-9197-9caf3fb14572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.plot(mdot_values, Ka1_values, label='beta=0.2')\n",
    "plt.plot(mdot_values, Ka2_values, label='beta=0.4')\n",
    "plt.plot(mdot_values, Ka3_values, label='beta=0.6')\n",
    "plt.plot(mdot_values, Ka4_values, label='beta=0.8')\n",
    "\n",
    "# 그래프 설정\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Mass Flow Rate (mdot_reactants)')\n",
    "plt.ylabel('Ka')\n",
    "plt.title('Mass Flow Rate vs. Strain rate')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
